About the role

Role Overview

Swish Analytics is hiring Data Engineers to support the infrastructure and delivery of its core consumer and enterprise data offerings, including coverage of non-US sports. This role focuses on low-latency, real-time predictive analytics and building production systems that power sports betting and fantasy products.

Responsibilities

Support and maintain production systems, including triaging issues during live sporting events.
Architect low-latency, real-time analytics systems, spanning:
- raw data collection
- feature development
- endpoint production
Build sports betting data products and predictive offerings.
Integrate large, complex real-time datasets into consumer and enterprise products.
Develop predictive analytics into production-grade APIs for enterprise use.
Design and implement fully automated sports data delivery frameworks.

Requirements

2+ years of experience writing production-level code (Python).
Strong proficiency in Python and SQL (preferably MySQL).
Experience with Airflow.
Experience with Kubernetes.
Experience building end-to-end ETL pipelines.
Experience using REST APIs.
Familiarity with Git, CI/CD, shell scripting, and AWS.
Experience with web scraping and cleaning unstructured data.
Knowledge of data science and machine learning concepts.
Strong interest in sports betting, with emphasis on tennis and knowledge of major US sports (e.g., NFL, NBA, MLB, NHL, college football/basketball) to apply context to complex datasets.

Nice to Have / Additional Notes

The role is remote.

Salary

$160,000+ (DOE)

About Swish Analytics

Swish Analytics is a sports analytics, betting, and fantasy startup focused on building predictive sports analytics data products. The company emphasizes real-time, accurate predictions driven by engineering and mathematics, not intuition, and delivers data offerings for both consumer and enterprise clients.

Tags